Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils
DOI:
https://doi.org/10.60084/ijds.v2i2.220Keywords:
ANN-PSO, Gas chromatography, Molecular descriptors, Optimization techniqueAbstract
The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research.
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References
- Zenkevich, I. G. (2010). Kovats’ Retention Index System, Encyclopedia of Chromatography, Vol. 2, 1304–1310.
- Yabalak, E., Ibrahim, F., and Erdoğan Eliuz, E. A. (2023). Natural Sanitizer Potential of Cuminum cyminum and Applicable Approach for Calculation of Kováts Retention Index of Its Compounds, International Journal of Environmental Health Research, Vol. 33, No. 2, 158–169. doi:10.1080/09603123.2021.2011159.
- Sadeghi, M., Mohammadinasab, E., and Momeni Isfahani, T. (2023). QSPR Models for Predicting the Kovats Retention Indices of Synthetic Ester Derivatives Based on Pyrethrin Essential Oil, Journal of Essential Oil Research, Vol. 35, No. 6, 542–562. doi:10.1080/10412905.2023.2265376.
- Aljaafari, M. N., AlAli, A. O., Baqais, L., Alqubaisy, M., AlAli, M., Molouki, A., Ong-Abdullah, J., Abushelaibi, A., Lai, K.-S., and Lim, S.-H. E. (2021). An Overview of the Potential Therapeutic Applications of Essential Oils, Molecules, Vol. 26, No. 3, 628. doi:10.3390/molecules26030628.
- Sadgrove, N., Padilla-González, G., and Phumthum, M. (2022). Fundamental Chemistry of Essential Oils and Volatile Organic Compounds, Methods of Analysis and Authentication, Plants, Vol. 11, No. 6, 789. doi:10.3390/plants11060789.
- Rojas, C., Cedillo, J. F., Sarmiento, N., Pis Diez, R., and Duchowicz, P. R. (2025). Quantitative Structure-Property Relationship for Gas-Chromatographic Retention Indices of Volatile Organic Compounds in Colored-Quinoa Seeds, Journal of Food Composition and Analysis, Vol. 137, 106843. doi:10.1016/j.jfca.2024.106843.
- Metrani, R., Jayaprakasha, G. K., and Patil, B. S. (2022). Optimization of Experimental Parameters and Chemometrics Approach to Identify Potential Volatile Markers in Seven Cucumis melo Varieties Using HS–SPME–GC–MS, Food Analytical Methods, Vol. 15, No. 3, 607–624. doi:10.1007/s12161-021-02119-9.
- Idroes, R., Noviandy, T. R., Maulana, A., Suhendra, R., and Sasmita, N. R. (2023). ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography, Infolitika Journal of Data Science, Vol. 1, No. 1, 1–14. doi:10.60084/ijds.v1i1.73.
- Noviandy, T. R., Idroes, G. M., and Hardi, I. (2024). An Interpretable Machine Learning Strategy for Antimalarial Drug Discovery with LightGBM and SHAP, Journal of Future Artificial Intelligence and Technologies, Vol. 1, No. 2, 84–95. doi:10.62411/faith.2024-16.
- Qu, C., Schneider, B. I., Kearsley, A. J., Keyrouz, W., and Allison, T. C. (2021). Predicting Kováts Retention Indices Using Graph Neural Networks, Journal of Chromatography A, Vol. 1646, 462100. doi:10.1016/j.chroma.2021.462100.
- Suhendra, R., Husdayanti, N., Suryadi, S., Juliwardi, I., Sanusi, S., Ridho, A., Ardiansyah, M., Murhaban, M., and Ikhsan, I. (2023). Cardiovascular Disease Prediction Using Gradient Boosting Classifier, Infolitika Journal of Data Science, Vol. 1, No. 2, 56–62. doi:10.60084/ijds.v1i2.131.
- Supriatna, D. J. I., Saputra, H., and Hasan, K. (2023). Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers, Infolitika Journal of Data Science, Vol. 1, No. 2, 42–47. doi:10.60084/ijds.v1i2.95.
- Noviandy, T. R., Nisa, K., Idroes, G. M., Hardi, I., and Sasmita, N. R. (2024). Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM, Journal of Computing Theories and Applications, Vol. 2, No. 2, 138–147. doi:10.62411/jcta.10129.
- Staszak, M., Staszak, K., Wieszczycka, K., Bajek, A., Roszkowski, K., and Tylkowski, B. (2022). Machine Learning in Drug Design: Use of Artificial Intelligence to Explore the Chemical Structure–Biological Activity Relationship, WIREs Computational Molecular Science, Vol. 12, No. 2. doi:10.1002/wcms.1568.
- Noviandy, T. R., Maulana, A., Idroes, G. M., Maulydia, N. B., Patwekar, M., Suhendra, R., and Idroes, R. (2023). Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer’s Disease Drug Discovery, Malacca Pharmaceutics, Vol. 1, No. 2, 48–54. doi:10.60084/mp.v1i2.60.
- Noviandy, T. R., Idroes, G. M., and Hardi, I. (2024). Enhancing Loan Approval Decision-Making: An Interpretable Machine Learning Approach Using LightGBM for Digital Economy Development, Malaysian Journal of Computing (MJOC), Vol. 9, No. 1, 1734–1745. doi:10.24191/mjoc.v9i1.25691.
- Petropoulos, A., Siakoulis, V., Stavroulakis, E., and Vlachogiannakis, N. E. (2020). Predicting Bank Insolvencies Using Machine Learning Techniques, International Journal of Forecasting, Vol. 36, No. 3, 1092–1113. doi:10.1016/j.ijforecast.2019.11.005.
- Noviandy, T. R., Maulana, A., Idroes, G. M., Suhendra, R., Adam, M., Rusyana, A., and Sofyan, H. (2023). Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet, Ekonomikalia Journal of Economics, Vol. 1, No. 1, 19–25. doi:10.60084/eje.v1i1.51.
- Wang, Z. H., Liu, Y. F., Wang, T., Wang, J. G., Liu, Y. M., and Huang, Q. X. (2024). Intelligent Prediction Model of Mechanical Properties of Ultrathin Niobium Strips Based on XGBoost Ensemble Learning Algorithm, Computational Materials Science, Vol. 231, 112579. doi:10.1016/j.commatsci.2023.112579.
- Hestroffer, J. M., Charpagne, M.-A., Latypov, M. I., and Beyerlein, I. J. (2023). Graph Neural Networks for Efficient Learning of Mechanical Properties of Polycrystals, Computational Materials Science, Vol. 217, 111894. doi:10.1016/j.commatsci.2022.111894.
- Westermayr, J., Gastegger, M., Schütt, K. T., and Maurer, R. J. (2021). Perspective on Integrating Machine Learning into Computational Chemistry and Materials Science, The Journal of Chemical Physics, Vol. 154, No. 23. doi:10.1063/5.0047760.
- Almeida, J. S. (2002). Predictive Non-linear Modeling of Complex Data by Artificial Neural Networks, Current Opinion in Biotechnology, Vol. 13, No. 1, 72–76. doi:10.1016/S0958-1669(02)00288-4.
- Wu, Y., and Feng, J. (2018). Development and Application of Artificial Neural Network, Wireless Personal Communications, Vol. 102, No. 2, 1645–1656. doi:10.1007/s11277-017-5224-x.
- Safhadi, A. A.-J., Noviandy, T. R., Irvanizam, I., Suhendra, R., Karma, T., and Idroes, R. (2024). Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds, Infolitika Journal of Data Science, Vol. 2, No. 1, 28–33. doi:10.60084/ijds.v2i1.197.
- Lorenzo, P. R., Nalepa, J., Kawulok, M., Ramos, L. S., and Pastor, J. R. (2017). Particle Swarm Optimization for Hyper-Parameter Selection in Deep Neural Networks, Proceedings of the Genetic and Evolutionary Computation Conference, ACM, New York, NY, USA, 481–488. doi:10.1145/3071178.3071208.
- Shariati, M., Mafipour, M. S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M. N. A., Nguyen, H., Dou, J., Song, X., and Poi-Ngian, S. (2019). Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete, Applied Sciences, Vol. 9, No. 24, 5534. doi:10.3390/app9245534.
- Jalal, F. E., Iqbal, M., Khan, W. A., Jamal, A., Onyelowe, K., and Lekhraj. (2024). ANN-Based Swarm Intelligence for Predicting Expansive Soil Swell Pressure and Compression Strength, Scientific Reports, Vol. 14, No. 1, 14597. doi:10.1038/s41598-024-65547-7.
- Babushok, V. I., Linstrom, P. J., and Zenkevich, I. G. (2011). Retention Indices for Frequently Reported Compounds of Plant Essential Oils, Journal of Physical and Chemical Reference Data, Vol. 40, No. 4, 043101. doi:10.1063/1.3653552.
- Noviandy, T. R., Idroes, G. M., Mohd Fauzi, F., and Idroes, R. (2024). Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery, Malacca Pharmaceutics, Vol. 2, No. 2, 68–78. doi:10.60084/mp.v2i2.217.
- Todeschini, R., and Consonni, V. (2000). Handbook of Molecular Descriptors, Wiley-VCH Verlag GmbH, Weinheim, Germany. doi:10.1002/9783527613106.
- Xue, L., and Bajorath, J. (2000). Molecular Descriptors in Chemoinformatics, Computational Combinatorial Chemistry, and Virtual Screening, Combinatorial Chemistry & High Throughput Screening, Vol. 3, No. 5, 363–372. doi:10.2174/1386207003331454.
- Noviandy, T. R., Maulana, A., Sasmita, N. R., Suhendra, R., Irvanizam, I., Muslem, M., Idroes, G. M., Yusuf, M., Sofyan, H., Abidin, T. F., and Idroes, R. (2022). The Prediction of Kovats Retention Indices of Essential Oils at Gas Chromatography Using Genetic Algorithm-Multiple Linear Regression and Support Vector Regression, Journal of Engineering Science and Technology, Vol. 17, No. 1, 306–326.
- Sushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Rupp, M., Teetz, W., Brandmaier, S., Abdelaziz, A., Prokopenko, V. V., Tanchuk, V. Y., Todeschini, R., Varnek, A., Marcou, G., Ertl, P., Potemkin, V., Grishina, M., Gasteiger, J., Schwab, C., Baskin, I. I., Palyulin, V. A., Radchenko, E. V., Welsh, W. J., Kholodovych, V., Chekmarev, D., Cherkasov, A., Aires-De-Sousa, J., Zhang, Q. Y., Bender, A., Nigsch, F., Patiny, L., Williams, A., Tkachenko, V., and Tetko, I. V. (2011). Online Chemical Modeling Environment (OCHEM): Web Platform for Data Storage, Model Development and Publishing of Chemical Information, Journal of Computer-Aided Molecular Design, Vol. 25, No. 6, 533–554. doi:10.1007/s10822-011-9440-2.
- Mswahili, M. E., Martin, G. L., Woo, J., Choi, G. J., and Jeong, Y.-S. (2021). Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum, Biomolecules, Vol. 11, No. 12, 1750. doi:10.3390/biom11121750.
- Maulana, A., Noviandy, T. R., Idroes, R., Sasmita, N. R., Suhendra, R., and Irvanizam, I. (2020). Prediction of Kovats Retention Indices for Fragrance and Flavor using Artificial Neural Network, 2020 International Conference on Electrical Engineering and Informatics (ICELTICs), IEEE, 1–5. doi:10.1109/ICELTICs50595.2020.9315391.
- Agustia, M., Noviandy, T. R., Maulana, A., Suhendra, R., Muslem, M., Sasmita, N. R., Idroes, G. M., Rahimah, S., Afidh, R. P. F., Subianto, M., Irvanizam, I., and Idroes, R. (2022). Application of Fuzzy Support Vector Regression to Predict the Kovats Retention Indices of Flavors and Fragrances, 2022 International Conference on Electrical Engineering and Informatics (ICELTICs), IEEE, 13–18. doi:10.1109/ICELTICs56128.2022.9932124.
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Copyright (c) 2024 Kurniadinur Kurniadinur, Teuku Rizky Noviandy, Ghazi Mauer Idroes, Noor Atinah Ahmad, Irvanizam Irvanizam , Muhammad Subianto, Rinaldi Idroes
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